Publications des scientifiques de l'IRD

Lievens H., Tomer S. K., Al Bitar A., De Lannoy G. J. M., Drusch M., Dumedah G., Franssen H. J. H., Kerr Yann, Martens B., Pan M., Roundy J. K., Vereecken H., Walker J. P., Wood E. F., Verhoest N. E. C., Pauwels V. R. N. (2015). SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia. Remote Sensing of Environment, 168, p. 146-162. ISSN 0034-4257.

Titre du document
SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia
Année de publication
2015
Type de document
Article référencé dans le Web of Science WOS:000361405500013
Auteurs
Lievens H., Tomer S. K., Al Bitar A., De Lannoy G. J. M., Drusch M., Dumedah G., Franssen H. J. H., Kerr Yann, Martens B., Pan M., Roundy J. K., Vereecken H., Walker J. P., Wood E. F., Verhoest N. E. C., Pauwels V. R. N.
Source
Remote Sensing of Environment, 2015, 168, p. 146-162 ISSN 0034-4257
This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Donnees SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125) and the SMOS observation (25 km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058 m(3)/m(3) to 0.046 m(3)/m(3) and increases the correlation from 0564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.
Plan de classement
Hydrologie [062] ; Télédétection [126]
Description Géographique
AUSTRALIE
Localisation
Fonds IRD
Identifiant IRD
PAR00013730
Contact